A connectionist approach to embodied conceptual metaphor

Abstract

A growing body of data has been gathered in support of the view that the mind is embodied and that cognition is grounded in sensory-motor processes. Some researchers have gone so far as to claim that this paradigm poses a serious challenge to central tenets of cognitive science, including the widely held view that the mind can be analyzed in terms of abstract computational principles. On the other hand, computational approaches to the study of mind have led to the development of specific models that help researchers understand complex cognitive processes at a level of detail that theories of embodied cognition (EC) have sometimes lacked. Here we make the case that connectionist architectures in particular can illuminate many surprising results from the EC literature. These models can learn the statistical structure in their environments, providing an ideal framework for understanding how simple sensory-motor mechanisms could give rise to higher-level cognitive behavior over the course of learning. Crucially, they form overlapping, distributed representations, which have exactly the properties required by many embodied accounts of cognition. We illustrate this idea by extending an existing connectionist model of semantic cognition in order to simulate findings from the embodied conceptual metaphor literature. Specifically, we explore how the abstract domain of time may be structured by concrete experience with space (including experience with culturally specific spatial and linguistic cues). We suggest that both EC researchers and connectionist modelers can benefit from an integrated approach to understanding these models and the empirical findings they seek to explain.

Keywords: conceptual metaphor; connectionism; embodiment; models; space; time.

Figure 1

Diagram of network architecture.

Figure 2

A diagram illustrating the structure of spatial and temporal relations in the model environment. The network learns about the consequences of both itself and other agents moving in the environment, though movement in the temporal domain is ambiguous.

Figure 3

The results of Simulation 1 showing that activating a particular spatial frame of reference biases the network's predictions about movement in the temporal domain.

Figure 4

The results of Simulation 2 showing that spatial relations that were never directly experienced in the temporal domain can still structure temporal reasoning.

Figure 5

The structure of the learned relation representation layer in Simulation 2.